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Wang Y, Tian X, Chevallier F, Johnson MS, Philip S, Baker DF, Schuh AE, Deng F, Zhang X, Zhang L, Zhu D, Wang X. Constraining China's land carbon sink from emerging satellite CO 2 observations: Progress and challenges. Glob Chang Biol 2022; 28:6838-6846. [PMID: 36324217 DOI: 10.1111/gcb.16412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/21/2022] [Indexed: 06/16/2023]
Abstract
Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space-based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2 , and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite-based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space-based CO2 measurements.
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Affiliation(s)
- Yilong Wang
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
| | - Xiangjun Tian
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy Sciences, Beijing, China
| | - Frédéric Chevallier
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France
| | - Matthew S Johnson
- Earth Science Division, NASA Ames Research Center, Moffett Field, California, USA
| | - Sajeev Philip
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
| | - David F Baker
- CIRA, Colorado State University, Fort Collins, Colorado, USA
| | - Andrew E Schuh
- CIRA, Colorado State University, Fort Collins, Colorado, USA
| | - Feng Deng
- Department of Physics, University of Toronto, Toronto, Ontario, Canada
| | - Xingying Zhang
- Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather) and Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration (CMA), Beijing, China
| | - Lu Zhang
- Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather) and Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration (CMA), Beijing, China
| | - Dan Zhu
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Xuhui Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
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Yang D, Boesch H, Liu Y, Somkuti P, Cai Z, Chen X, Di Noia A, Lin C, Lu N, Lyu D, Parker RJ, Tian L, Wang M, Webb A, Yao L, Yin Z, Zheng Y, Deutscher NM, Griffith DWT, Hase F, Kivi R, Morino I, Notholt J, Ohyama H, Pollard DF, Shiomi K, Sussmann R, Té Y, Velazco VA, Warneke T, Wunch D. Toward High Precision XCO 2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements. J Geophys Res Atmos 2020; 125:e2020JD032794. [PMID: 33777605 PMCID: PMC7983077 DOI: 10.1029/2020jd032794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 07/22/2020] [Accepted: 07/24/2020] [Indexed: 06/12/2023]
Abstract
TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO2 processing.
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Affiliation(s)
- D. Yang
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - H. Boesch
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - Y. Liu
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - P. Somkuti
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
- Colorado State UniversityFort CollinsCOUSA
| | - Z. Cai
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - X. Chen
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - A. Di Noia
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - C. Lin
- Changchun Institute of Optics, Fine Mechanics and PhysicsChina
| | - N. Lu
- National Satellite Meteorological Center, China Meteorological AdministrationChina
| | - D. Lyu
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - R. J. Parker
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - L. Tian
- Shanghai Engineering Center for MicrosatellitesChina
| | - M. Wang
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - A. Webb
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - L. Yao
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - Z. Yin
- Shanghai Engineering Center for MicrosatellitesChina
| | - Y. Zheng
- Changchun Institute of Optics, Fine Mechanics and PhysicsChina
| | - N. M. Deutscher
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - D. W. T. Griffith
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - F. Hase
- Karlsruhe Institute of Technology, IMK‐IFUGarmisch‐PartenkirchenGermany
| | - R. Kivi
- Space and Earth Observation CentreFinnish Meteorological InstituteFinland
| | - I. Morino
- National Institute for Environmental Studies (NIES)TsukubaIbarakiJapan
| | - J. Notholt
- Institute of Environmental Physics (IUP)University of BremenBremenGermany
| | - H. Ohyama
- National Institute for Environmental Studies (NIES)TsukubaIbarakiJapan
| | - D. F. Pollard
- National Institute of Water and Atmospheric Research Ltd (NIWA)LauderNew Zealand
| | - K. Shiomi
- Japan Aerospace Exploration AgencyJapan
| | - R. Sussmann
- Karlsruhe Institute of Technology, IMK‐IFUGarmisch‐PartenkirchenGermany
| | - Y. Té
- Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA‐IPSL)Sorbonne Université, CNRS, Observatoire de Paris, PSL UniversitéParisFrance
| | - V. A. Velazco
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - T. Warneke
- Institute of Environmental Physics (IUP)University of BremenBremenGermany
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Karthikeyan L, Pan M, Nagesh Kumar D, Wood EF. Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm. Sensors (Basel) 2020; 20:E1225. [PMID: 32102289 DOI: 10.3390/s20041225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 02/05/2020] [Accepted: 02/18/2020] [Indexed: 11/17/2022]
Abstract
Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
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Wang H, Li X, Xu J, Zhang X, Ge S, Chen L, Wang Y, Zhu S, Miao J, Si Y. Assessment of Retrieved N₂O, NO₂, and HF Profiles from the Atmospheric Infrared Ultraspectral Sounder Based on Simulated Spectra. Sensors (Basel) 2018; 18:E2209. [PMID: 29987268 DOI: 10.3390/s18072209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/27/2018] [Accepted: 07/06/2018] [Indexed: 11/16/2022]
Abstract
The Atmospheric Infrared Ultraspectral Sounder (AIUS), the first high-resolution (0.02 cm−1) solar occultation sounder, aboard GF5, was launched in May 2018 from China. However, relevant studies about vertical profiles of atmospheric constituents based on its operational data were not conducted until half a year later. Due to an urgent need for Hin-orbit tests, the real spectra (called reference spectra hereafter) were substituted with simulated spectra calculated from the reference forward model (RFM) plus different random noises at different altitudes. In the generation process of the reference spectra for N₂O, NO₂, and HF species, ACE-FTS (Atmospheric Chemistry Experiment⁻Fourier Transform Spectrometer instrument on the SCISAT satellite) level 2 products replace corresponding profiles included in the atmospheric background profiles. The optimal estimation method is employed to extract N₂O, NO₂, and HF profiles in this study. Comparing the retrieved results with ACE-FTS level 2 products, the relative deviations for these three species are calculated. For N₂O, the average relative deviation is less than 6% at altitudes below 25 km, while larger deviations are observed in the range of 25⁻45 km, with the maximum being at ~25%. Additionally, the difference for NO₂ is less than 5% in the 20⁻45 km range, with a larger discrepancy found below 20 km and above 45 km; the maximum deviation reaches ±40%. For HF, the relative deviation is less than 6% for all tangent heights, implying satisfactory retrieval. The vertical resolution, averaging kernel, and number of degrees of freedom are used to assess the retrieval algorithm, which indicate that the retrieved information content is much more attributable to the reference spectra contribution than to the a priori profile. Finally, a large number of retrieval tests are performed for N₂O, NO₂, and HF in selected areas covering the Arctic region, northern middle latitude, tropics, southern middle latitude, and Antarctic region, and reliable results are obtained. Thus, to a great extent, the algorithm adopted in the AIUS system can process retrievals reliably and precisely.
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Liu Y, Yamaguchi Y, Ke C. Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products. Sensors (Basel) 2007; 7:3043-3057. [PMID: 28903278 PMCID: PMC3841879 DOI: 10.3390/s7123043] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2007] [Accepted: 12/02/2007] [Indexed: 11/27/2022]
Abstract
Human-induced global warming has significantly increased the importance of satellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST product with almost daily coverage of the Earth, invaluable to both local and global change studies. The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides a LST product with a high spatial resolution of 90-m and a 16-day recurrent cycle, simultaneously acquired at the same height and nadir view as MODIS. ASTER and MODIS are complementary in resolution, offering a unique opportunity for scale-related studies. ASTER and MODIS LST have been widely used but the errors in LST were mostly disregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studies reliant upon the joint use of these sensors. In this study, we compared three correction approaches: the Wan et al.'s approach, the refined Wan et al.'s approach, and the generalized split window (GSW) algorithm based approach. The Wan et al.'s approach corrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifies the Wan et al.'s approach through incorporating ASTER emissivity and MODIS 5-km data. The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient.
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Affiliation(s)
- Yuanbo Liu
- Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
| | - Yasushi Yamaguchi
- Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
| | - Changqing Ke
- Department of Geographical Information Science, Nanjing University, Hankou Road 22, Nanjing, 210093, China.
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